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An novel data-driven structural optimisation method for engineering and science

Study level

PhD

Master of Philosophy

Honours

Vacation research experience scheme

Faculty/Lead unit

Science and Engineering Faculty

Science and Engineering Faculty

Topic status

We're looking for students to study this topic.

Supervisors

Professor YuanTong Gu
Position
Professor
Division / Faculty
Science and Engineering Faculty

Overview

Structural optimisation, an effective design method for light-weight and high-performance structures, traditionally relies on the computational mechanics method with empirical constitutive model of related material.

However, the empirical constitutive modelling remains an open question worldwide for a long term. The imperfect knowledge of constitutive laws and empiricism and arbitrariness of constitutive modelling process limit the applications of structural optimisation methods in advanced material structures.

Research activities

The project aims at developing a structural optimization method based on the data-driven computational mechanics, which bypasses the empirical modelling process and directly incorporates experimental data in calculations.

In this manner, the modelling empiricism, error and uncertainty are eliminated and loss of experimental information is thus avoided in structural optimization design.

Outcomes

This project will develop a novel structural optimisation method based on the data-driven computational mechanics. This will lead to a breakthrough in computer modelling method in engineering and science based on the recent development of big data technologies.

Scholarships

You may be able to apply for a research scholarship in our annual scholarship round.

Annual scholarship round

Keywords

Contact

Contact the supervisor for more information.